Italy DI: HH: HP: CA: Other Sources of Finance data was reported at 0.000 Index in Jul 2018. This stayed constant from the previous number of 0.000 Index for Apr 2018. Italy DI: HH: HP: CA: Other Sources of Finance data is updated quarterly, averaging 0.000 Index from Jan 2003 (Median) to Jul 2018, with 63 observations. The data reached an all-time high of 0.083 Index in Apr 2007 and a record low of -0.063 Index in Apr 2013. Italy DI: HH: HP: CA: Other Sources of Finance data remains active status in CEIC and is reported by Bank of Italy. The data is categorized under Global Database’s Italy – Table IT.KB015: Bank Lending Survey.

This statistic shows the risk index of money laundering and terrorist financing in the Benelux countries (Belgium, Luxembourg and the Netherlands) from 2016 to 2022. In 2022, the Netherlands was ranked as the country with the highest risk in the Benelux.

The Basel AML Index is a composite index, a combination of 14 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.

Daily IHSG (Jakarta Composite Index) Historical Data

This dataset contains daily historical prices for the IHSG (Indeks Harga Saham Gabungan), also known as the Jakarta Composite Index (JCI). IHSG is the main stock market index of the Indonesia Stock Exchange (IDX) and is widely used as a benchmark for the Indonesian equity market.

The dataset is intended for:

• Time series analysis and forecasting
• Financial modeling and backtesting
• Market behavior and volatility analysis
• Educational projects and tutorials in quantitative finance

Data Source and Disclaimer

• Original source: Yahoo Finance
• Ticker: ^JKSE (Jakarta Composite Index)
• Source URL: https://finance.yahoo.com/quote/%5EJKSE/

The data was downloaded from Yahoo Finance and then exported to CSV format.

Disclaimer:
This dataset repackages market data originally provided by Yahoo Finance and its data vendors. It is shared for research and educational purposes only. Yahoo’s Terms of Service and the data providers’ terms may restrict redistribution and commercial use. Users are responsible for ensuring that their use of this dataset complies with all applicable terms and laws. Past performance is not indicative of future results.

File Overview

File: ihsg_daily.csv

• Rows: ~8k daily records
• Columns: 5
• Date range: 1995-01-02 to 2025-10-06 (trading days only)

Columns

• Date
  • Trading date (originally M/D/YYYY in the source; may appear as YYYY-MM-DD depending on your environment).
• Open
  • Opening index level for that trading day.
• High
  • Highest index level reached during the trading day.
• Low
  • Lowest index level reached during the trading day.
• Close
  • Closing index level for that trading day.

There are no Adj Close or Volume columns in this CSV file.

In 2024, Peru had an index score of 4.77, down from 4.77 reported the year before. In recent years, the risk index of this South American country showed an upward trend. Peru was ranked as one of the countries with the lowest risk index scores of money laundering and terrorist financing in Latin America.The Basel AML Index is a composite index, a combination of 14 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.

In 2024, Grenada had an index score of 4.72, down from 4.97 the year before. Further, Grenada was ranked as the country with the tenth lowest risk index of money laundering and terrorist financing in Latin America.The Basel AML Index is a composite index, a combination of 16 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.

For the second year running, Ecuador's score on the money laundering and terrorist financing risk index remained the same. In both 2023 and 2024, Ecuador ranked with an index score of 5.06. The Basel AML Index is a composite index, a combination of 16 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.

Venezuela reached, in 2021, an index score of 6.29, a slight decrease from the peak recorded in the previous year. In 2024, Venezuela also ranked among the countries with the highest risk index of money laundering and terrorist financing in Latin America. The Basel AML Index is a composite index, a combination of 16 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Graph and download economic data for Chicago Fed National Financial Conditions Index (NFCI) from 1971-01-08 to 2025-11-21 about financial, indexes, and USA.

View market daily updates and historical trends for Secured Overnight Financing Rate. from United States. Source: Federal Reserve Bank of New York. Track …

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Hedge funds are private, unregulated investment funds that use sophisticated instruments or strategies, such as derivative securities, short positions or leveraging, to generate alpha. Hedge funds cover a wide range of strategies with different risk and return profiles.

About This Dataset

Data Date: 1997/1 - 2021/6 Columns : 13 Different Investing Style Index Value : Monthly Return

Description

Convertible Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/conv_arb.pdf CTA Global : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/cta.pdf Distressed Securities : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/distressed.pdf Emerging Markets : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/emerging.pdf Equity Market Neutral : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/market_ntl.pdf Event Driven : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/event_driven.pdf Fixed Income Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/fix_inc.pdf Global Macro : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/global_macro.pdf Long/Short Equity : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/long_short.pdf Merger Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/merger.pdf Relative Value : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/value.pdf Short Selling : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/short.pdf Funds of Funds : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/fof.pdf

Credit To

Data Source :EDHEC-Risk Institute Since 2003, EDHEC-Risk Institute has been publishing the EDHEC-Risk Alternative Indices, which aggregate and synthesise information from different index providers, so as to provide investors with representative benchmarks. These indices are computed for thirteen investment styles that represent typical hedge fund strategies. https://risk.edhec.edu/all-downloads-hedge-funds-indices

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Resources Financing.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Tibet was excluded from the sample. The excluded areas represent less than 1 percent of the total population of China.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for China is 3500.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

China Banks' Wealth Management Product Sentiment Index (BWMPSI) data was reported at 5,856.250 Jan2009=100 in Jul 2017. This records an increase from the previous number of 5,552.841 Jan2009=100 for Jun 2017. China Banks' Wealth Management Product Sentiment Index (BWMPSI) data is updated monthly, averaging 1,716.476 Jan2009=100 from Jan 2009 (Median) to Jul 2017, with 103 observations. The data reached an all-time high of 6,511.364 Jan2009=100 in Mar 2017 and a record low of 100.000 Jan2009=100 in Jan 2009. China Banks' Wealth Management Product Sentiment Index (BWMPSI) data remains active status in CEIC and is reported by Institute of Finance and Banking, Chinese Academy of Social Sciences. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series. Since July 2017, the data source (Institute of Finance and Banking, Chinese Academy of Social Sciences) ceased to provide data to CEIC, and the data of this series was therefore no longer updated.

China BWMPSI: RMB Floating Income Product data was reported at 2,866.667 Jan2009=100 in Jul 2017. This records a decrease from the previous number of 3,644.444 Jan2009=100 for Jun 2017. China BWMPSI: RMB Floating Income Product data is updated monthly, averaging 1,388.889 Jan2009=100 from Jan 2009 (Median) to Jul 2017, with 103 observations. The data reached an all-time high of 4,166.667 Jan2009=100 in Dec 2015 and a record low of 100.000 Jan2009=100 in Jan 2009. China BWMPSI: RMB Floating Income Product data remains active status in CEIC and is reported by Institute of Finance and Banking, Chinese Academy of Social Sciences. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series. Since July 2017, the data source (Institute of Finance and Banking, Chinese Academy of Social Sciences) ceased to provide data to CEIC, and the data of this series was therefore no longer updated.

Graph and download economic data for Money Market Funds; Total Financial Assets, Level (MMMFFAQ027S) from Q4 1945 to Q2 2025 about MMMF, IMA, financial, assets, and USA.

China BWMPII: RMB Fixed Income Product data was reported at 187.172 Jan2009=100 in Jul 2017. This records a decrease from the previous number of 187.273 Jan2009=100 for Jun 2017. China BWMPII: RMB Fixed Income Product data is updated monthly, averaging 173.310 Jan2009=100 from Jan 2009 (Median) to Jul 2017, with 103 observations. The data reached an all-time high of 236.038 Jan2009=100 in Jan 2014 and a record low of 83.414 Jan2009=100 in Feb 2009. China BWMPII: RMB Fixed Income Product data remains active status in CEIC and is reported by Institute of Finance and Banking, Chinese Academy of Social Sciences. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series. Since July 2017, the data source (Institute of Finance and Banking, Chinese Academy of Social Sciences) ceased to provide data to CEIC, and the data of this series was therefore no longer updated.

this graph was created in Tableau,PowerBi and Loocker.

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The Shanghai Composite Index (SSE), as a representative composite index of listed companies on the Shanghai Stock Exchange, is a core observation indicator of the systematic risk and price discovery mechanism in China's capital market. It includes various industries such as finance, energy, and industry, and can effectively depict the overall dynamic changes of the market This study selected intraday high-frequency data from January 2, 2024 to December 31, 2024. In order to accurately capture tail extreme events (such as liquidity shocks or policy driven jump risks) and overcome the discontinuity problem caused by low-frequency sampling, a balanced data frequency with 5-minute intervals was adopted The final dataset covers 48 observation points for each trading day, obtaining a total of 11656 observations of index returns within effective days Meanwhile, Monetary policy and real estate policy are the core tools of macroeconomic regulation. The former directly affects market liquidity, interest rates, and financing costs, while the latter, as a pillar industry of China's economy, directly affects market stability. Therefore, this article takes the release of information on monetary policy and real estate policy as representative events of macroeconomic policy, and adopts the event study method (Sorescu et al. (2017)) to ultimately determine 25 positive policies and 16 negative policies The price data of the Shanghai Composite Index was purchased from the financial data service of Jinshu Source（ http://www.jinshuyuan.net/pdt/196 ）, the monetary policy announcement was collected from the official website of the People's Bank of China（ http://www.pbc.gov.cn/zhengcehuobisi ）The real estate regulation policy documents are integrated from China Real Estate Network（ http://m.fangchan.com/data ).

View quarterly updates and historical trends for Ogden, UT House Price All-Transactions Index. Source: Federal Housing Finance Agency. Track economic data…